Hierarchical Failure Analysis and Digital Twin Modelling of Next-Gen Hydrogel Coatings

About the project

About the Project This project is part of cohort 4 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute. This PhD Project sets the foundation for a new class of smart, durable hydrogel coatings designed using digital-first principles. By fusing materials science, machine learning, and advanced simulation, the project offers an exciting opportunity to redefine how we engineer and deploy functional surfaces for next-generation biomedical and medical implants. The project is supported by university of Leeds, university of Strathclyde and CN Technology Service Ltd. Background Hydrogel coatings represent a critical interface technology for biomedical applications, yet their widespread adoption is severely limited by fundamental challenges in adhesion durability and mechanical integrity under real-world cyclic loading conditions. Structural limitations in current laboratory tests mean that hydrogel-coated prototypes cannot always be mechanically tested, complicating long-term performance evaluation for commercial development. Research objectives This project proposes developing an integrated digital twin platform that bridges experimental characterisation, multi-physics modelling, and deep learning to enable predictive design and optimization of hydrogel coating systems. The innovation lies in establishing a closed-loop workflow specifically for interfacial mechanics, with multi-scale integration and physics-informed machine learning and experimental design optimisation. Training and Career Development You will gain interdisciplinary skills in machine learning, medical image analysis, and coating design. As a PhD student within the University of Leeds and co-supervised by staff at the University of Strathclyde and CN Technology Service Ltd, there will be opportunities to contribute to wider activities related to precision measurement and transferable skill training. Groups of researchers working on aligned projects or using similar methods meet regularly to share ideas and best practice. We will support student long-term career ambitions through bespoke training and encourage external secondments, laboratory visits or participation at international conferences. Skills Required Candidates should hold a good bachelor’s degree (first or upper second-class honours degree) or an MEng/MSc degree in a relevant engineering/science subject. Funding Notes This is a fully-funded project, part of cohort 4 of the EPSRC CDT in Developing National Capabilities in Materials 4.0. The studentship covers fees (home), a tax-free stipend of at least £21,805 plus London allowance if applicable, and a research training support grant. Enquiries For general enquiries, please contact doctoral-training@royce.ac.uk . For application-related queries, please contact phd@engineering.leeds.ac.uk . For project-related queries, please contact the lead supervisor, Dr Shufan Yang ( S.F.Yang@leeds.ac.uk ​). Application Process Please note that each partner of the Materials 4.0 CDT will have its own application process. The Materials 4.0 CDT is committed to Equality, Diversity and Inclusion. We strongly encourage applications from underrepresented groups. As an international research-intensive university, the University of Leeds welcomes students from all walks of life. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+, and people with disabilities. Applicants will always be selected based on merit and ability. Application Web Page https://prod.banner.leeds.ac.uk/ssb/bwskaloguol.PDispLoginNon Select research degree - research postgraduate, then 2027/28 academic year and, under 'planned course of study', choose 'EPSRC CDT Materials 4.0'.

Research areas